# Installing the libraries with the specified version
#!pip install --no-deps tensorflow==2.18.0 scikit-learn==1.3.2 matplotlib===3.8.3 seaborn==0.13.2 numpy==1.26.4 pandas==2.2.2 -q --user --no-warn-script-location

#Library for data manupulation and analysis
import numpy as np
#Package for scientific computation
import pandas as pd
#Library for data visualization
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
#Splitting dataset in training and test
from sklearn.model_selection import train_test_split
#Time function
import time
#import  tools for data processing
from sklearn.preprocessing import StandardScaler,LabelEncoder,OneHotEncoder
#import simplerimputer
from sklearn.impute import SimpleImputer
#Import function for evaluating performance of model
from sklearn.metrics import f1_score,accuracy_score,precision_score,recall_score,classification_report
#import metrics
from sklearn import metrics
#Imports the tensorflow,keras and layers
import tensorflow
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential # Corrected import statement
from tensorflow.keras.layers import Dense,Dropout,Input,BatchNormalization
from tensorflow.keras.optimizers import Adam,SGD
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import backend
# to suppress unnecessary warnings
import warnings
warnings.filterwarnings("ignore")

# Set the seed using keras.utils.set_random_seed. This will set:
# 1) `numpy` seed
# 2) backend random seed
# 3) `python` random seed
keras.utils.set_random_seed(812)

# If using TensorFlow, this will make GPU ops as deterministic as possible,
# but it might affect the overall performance
tf.config.experimental.enable_op_determinism()

# uncomment and run the following lines for Google Colab
#from google.colab import drive
#drive.mount('/content/drive')

data=pd.read_csv('Train.csv')
data_test=pd.read_csv('Test.csv')

#creating copies
df=data.copy()
df_test=data_test.copy()

#shape of the Train dataset
print(f'The Train dataset has {df.shape[0]} rows and {df.shape[1]} columns ')

The Train dataset has 20000 rows and 41 columns

#shape of the Test dataset
print(f'The Test dataset has {df_test.shape[0]} rows and {df_test.shape[1]} columns ')

The Test dataset has 5000 rows and 41 columns

#First five raws of train
df.head()

#First five raws of test
df_test.head()

#datatype for train
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20000 entries, 0 to 19999
Data columns (total 41 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   V1      19982 non-null  float64
 1   V2      19982 non-null  float64
 2   V3      20000 non-null  float64
 3   V4      20000 non-null  float64
 4   V5      20000 non-null  float64
 5   V6      20000 non-null  float64
 6   V7      20000 non-null  float64
 7   V8      20000 non-null  float64
 8   V9      20000 non-null  float64
 9   V10     20000 non-null  float64
 10  V11     20000 non-null  float64
 11  V12     20000 non-null  float64
 12  V13     20000 non-null  float64
 13  V14     20000 non-null  float64
 14  V15     20000 non-null  float64
 15  V16     20000 non-null  float64
 16  V17     20000 non-null  float64
 17  V18     20000 non-null  float64
 18  V19     20000 non-null  float64
 19  V20     20000 non-null  float64
 20  V21     20000 non-null  float64
 21  V22     20000 non-null  float64
 22  V23     20000 non-null  float64
 23  V24     20000 non-null  float64
 24  V25     20000 non-null  float64
 25  V26     20000 non-null  float64
 26  V27     20000 non-null  float64
 27  V28     20000 non-null  float64
 28  V29     20000 non-null  float64
 29  V30     20000 non-null  float64
 30  V31     20000 non-null  float64
 31  V32     20000 non-null  float64
 32  V33     20000 non-null  float64
 33  V34     20000 non-null  float64
 34  V35     20000 non-null  float64
 35  V36     20000 non-null  float64
 36  V37     20000 non-null  float64
 37  V38     20000 non-null  float64
 38  V39     20000 non-null  float64
 39  V40     20000 non-null  float64
 40  Target  20000 non-null  int64  
dtypes: float64(40), int64(1)
memory usage: 6.3 MB

#datatype for test
df_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 41 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   V1      4995 non-null   float64
 1   V2      4994 non-null   float64
 2   V3      5000 non-null   float64
 3   V4      5000 non-null   float64
 4   V5      5000 non-null   float64
 5   V6      5000 non-null   float64
 6   V7      5000 non-null   float64
 7   V8      5000 non-null   float64
 8   V9      5000 non-null   float64
 9   V10     5000 non-null   float64
 10  V11     5000 non-null   float64
 11  V12     5000 non-null   float64
 12  V13     5000 non-null   float64
 13  V14     5000 non-null   float64
 14  V15     5000 non-null   float64
 15  V16     5000 non-null   float64
 16  V17     5000 non-null   float64
 17  V18     5000 non-null   float64
 18  V19     5000 non-null   float64
 19  V20     5000 non-null   float64
 20  V21     5000 non-null   float64
 21  V22     5000 non-null   float64
 22  V23     5000 non-null   float64
 23  V24     5000 non-null   float64
 24  V25     5000 non-null   float64
 25  V26     5000 non-null   float64
 26  V27     5000 non-null   float64
 27  V28     5000 non-null   float64
 28  V29     5000 non-null   float64
 29  V30     5000 non-null   float64
 30  V31     5000 non-null   float64
 31  V32     5000 non-null   float64
 32  V33     5000 non-null   float64
 33  V34     5000 non-null   float64
 34  V35     5000 non-null   float64
 35  V36     5000 non-null   float64
 36  V37     5000 non-null   float64
 37  V38     5000 non-null   float64
 38  V39     5000 non-null   float64
 39  V40     5000 non-null   float64
 40  Target  5000 non-null   int64  
dtypes: float64(40), int64(1)
memory usage: 1.6 MB

#Checking for missing values in train
df.isnull().sum()

#Checking for missing values in test
df_test.isnull().sum()

#Duplicates in train
df.duplicated().sum()

np.int64(0)

#statistical summary of train
df.describe().T

def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

cols_list = df.select_dtypes(include=np.number).columns.tolist()
cols_list.remove("Target")
for feature in cols_list:
    histogram_boxplot(df, feature)

#Distribution in Train
df.Target.value_counts(normalize=True)

#Distribution in test
df_test.Target.value_counts(normalize=True)

#Plot correlation between features
plt.figure(figsize=(30,20))
sns.heatmap(df[cols_list].corr(),annot=True,vmin=-1,vmax=1,cmap='BrBG')
plt.show()

### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()

for feature in cols_list:
    distribution_plot_wrt_target(df, feature, 'Target')

X=df.drop('Target',axis=1)
y=df['Target']

#printing the shape of the data
print(X.shape)
print(y.shape)

(20000, 40)
(20000,)

X_train,X_val,y_train,y_val=train_test_split(X,y,test_size=0.2,random_state=1,stratify=y)

#checking the shape of dataset
print(X_train.shape)
print(X_val.shape)
print(y_train.shape)
print(y_val.shape)

(16000, 40)
(4000, 40)
(16000,)
(4000,)

X_test=df_test.drop('Target',axis=1)
y_test=df_test['Target']

print(X_test.shape)
print(y_test.shape)

(5000, 40)
(5000,)

#defining SimpleImputer
imputer=SimpleImputer(strategy='median')

#Fit and transform training data
X_train=pd.DataFrame(imputer.fit_transform(X_train),columns=X_train.columns)
#Transform validation data
X_val=pd.DataFrame(imputer.transform(X_val),columns=X_train.columns)
#Transform test data
X_test=pd.DataFrame(imputer.transform(X_test),columns=X_train.columns)

#checking missing values
print(X_train.isnull().sum().sum())
print(X_val.isnull().sum().sum())
print(X_test.isnull().sum().sum())

0
0
0

# Calculate class weights for imbalanced dataset
cw = (y_train.shape[0]) / np.bincount(y_train)

# Create a dictionary mapping class indices to their respective class weights
cw_dict = {}
for i in range(cw.shape[0]):
    cw_dict[i] = cw[i]

cw_dict

{0: np.float64(1.0587612493382743), 1: np.float64(18.01801801801802)}

def plot(history, name):
    """
    Function to plot loss/accuracy

    history: an object which stores the metrics and losses.
    name: can be one of Loss or Accuracy
    """
    #Creating a subplot with figure and axes.
    fig, ax = plt.subplots()
    #Plotting the train accuracy or train loss
    plt.plot(history.history[name])
    #Plotting the validation accuracy or validation loss
    plt.plot(history.history['val_'+name])
    #Defining the title of the plot.
    plt.title('Model ' + name.capitalize())
    #Capitalizing the first letter.
    plt.ylabel(name.capitalize())
    #Defining the label for the x-axis.
    plt.xlabel('Epoch')
    #Defining the legend, loc controls the position of the legend.
    fig.legend(['Train', 'Validation'], loc="outside right upper")

# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred = model.predict(predictors) > threshold

    # to compute Accuracy
    acc = accuracy_score(target, pred)
    # to compute Recall
    recall = recall_score(target, pred, average='weighted')
    # to compute Precision
    precision = precision_score(target, pred, average='weighted')
    # to compute F1-score
    f1 = f1_score(target, pred, average='weighted')

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1 Score": f1,},
        index=[0],
    )

    return df_perf

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_0 = Sequential()
model_0.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_0.add(Dense(32,activation="relu"))
model_0.add(Dense(1,activation = 'sigmoid'))
#compiling the model With SDG as optimiser
optimizer=keras.optimizers.SGD()
model_0.compile(optimizer = optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_0.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.7752 - recall: 0.8306 - val_loss: 0.1977 - val_recall: 0.8784
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.4029 - recall: 0.9043 - val_loss: 0.1678 - val_recall: 0.8874
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3533 - recall: 0.9128 - val_loss: 0.1621 - val_recall: 0.8874
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.3282 - recall: 0.9154 - val_loss: 0.1571 - val_recall: 0.8964
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3092 - recall: 0.9151 - val_loss: 0.1509 - val_recall: 0.8964
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2935 - recall: 0.9169 - val_loss: 0.1526 - val_recall: 0.8964
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2805 - recall: 0.9229 - val_loss: 0.1490 - val_recall: 0.9009
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2688 - recall: 0.9239 - val_loss: 0.1413 - val_recall: 0.8964
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2600 - recall: 0.9268 - val_loss: 0.1406 - val_recall: 0.8964
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2516 - recall: 0.9235 - val_loss: 0.1320 - val_recall: 0.8919
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2394 - recall: 0.9283 - val_loss: 0.1305 - val_recall: 0.8919
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2364 - recall: 0.9276 - val_loss: 0.1269 - val_recall: 0.8964
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2307 - recall: 0.9262 - val_loss: 0.1292 - val_recall: 0.8964
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2222 - recall: 0.9312 - val_loss: 0.1287 - val_recall: 0.8964
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2169 - recall: 0.9345 - val_loss: 0.1241 - val_recall: 0.8964
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2113 - recall: 0.9346 - val_loss: 0.1278 - val_recall: 0.8964
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2050 - recall: 0.9321 - val_loss: 0.1218 - val_recall: 0.8964
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1989 - recall: 0.9364 - val_loss: 0.1162 - val_recall: 0.8919
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1991 - recall: 0.9341 - val_loss: 0.1185 - val_recall: 0.8919
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1910 - recall: 0.9348 - val_loss: 0.1173 - val_recall: 0.8919
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1849 - recall: 0.9361 - val_loss: 0.1266 - val_recall: 0.8919
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1792 - recall: 0.9354 - val_loss: 0.1149 - val_recall: 0.8919
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1748 - recall: 0.9387 - val_loss: 0.1168 - val_recall: 0.8874
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1777 - recall: 0.9390 - val_loss: 0.1129 - val_recall: 0.8874
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.1718 - recall: 0.9430 - val_loss: 0.1184 - val_recall: 0.8964
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1693 - recall: 0.9380 - val_loss: 0.1212 - val_recall: 0.8919
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1673 - recall: 0.9401 - val_loss: 0.1171 - val_recall: 0.8829
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1642 - recall: 0.9462 - val_loss: 0.1193 - val_recall: 0.8874
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1463 - recall: 0.9449 - val_loss: 0.1223 - val_recall: 0.8874
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1685 - recall: 0.9389 - val_loss: 0.1120 - val_recall: 0.8874

print("Time taken in seconds ",end-start)

Time taken in seconds  32.86089372634888

model_0.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 4,739 (18.52 KB)

 Trainable params: 4,737 (18.50 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 2 (12.00 B)

plot(history,"loss")

model_0_train_perf=model_performance_classification(model_0,X_train,y_train)
model_0_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 709us/step

model_0_val_perf=model_performance_classification(model_0,X_val,y_val)
model_0_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_1 = Sequential()
model_1.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_1.add(Dense(32,activation="relu"))
model_1.add(Dense(1,activation = 'sigmoid'))
#compiling the model With SDG with momentum as optimiser
optimizer =keras.optimizers.SGD(momentum=0.9)
model_1.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_1.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.7851 - recall: 0.8329 - val_loss: 0.2611 - val_recall: 0.8919
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.3576 - recall: 0.9007 - val_loss: 0.2256 - val_recall: 0.9054
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.3194 - recall: 0.9109 - val_loss: 0.1662 - val_recall: 0.8964
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3158 - recall: 0.9217 - val_loss: 0.1611 - val_recall: 0.8829
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2944 - recall: 0.9186 - val_loss: 0.1425 - val_recall: 0.8694
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2862 - recall: 0.9219 - val_loss: 0.1462 - val_recall: 0.8874
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2694 - recall: 0.9239 - val_loss: 0.1583 - val_recall: 0.8919
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2779 - recall: 0.9220 - val_loss: 0.1526 - val_recall: 0.8919
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2605 - recall: 0.9287 - val_loss: 0.1419 - val_recall: 0.8784
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2522 - recall: 0.9317 - val_loss: 0.1548 - val_recall: 0.8829
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2517 - recall: 0.9310 - val_loss: 0.1554 - val_recall: 0.8784
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2498 - recall: 0.9317 - val_loss: 0.1703 - val_recall: 0.8829
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2430 - recall: 0.9317 - val_loss: 0.1766 - val_recall: 0.8964
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2397 - recall: 0.9350 - val_loss: 0.1976 - val_recall: 0.8919
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.2573 - recall: 0.9277 - val_loss: 0.1822 - val_recall: 0.8964
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2624 - recall: 0.9342 - val_loss: 0.3325 - val_recall: 0.8919
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2957 - recall: 0.9233 - val_loss: 0.1703 - val_recall: 0.8874
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2483 - recall: 0.9317 - val_loss: 0.2299 - val_recall: 0.8829
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2435 - recall: 0.9343 - val_loss: 0.2095 - val_recall: 0.8784
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2363 - recall: 0.9364 - val_loss: 0.2124 - val_recall: 0.8829
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2370 - recall: 0.9364 - val_loss: 0.2471 - val_recall: 0.8874
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2423 - recall: 0.9373 - val_loss: 0.2205 - val_recall: 0.8874
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2358 - recall: 0.9371 - val_loss: 0.2316 - val_recall: 0.8784
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2812 - recall: 0.9287 - val_loss: 0.2032 - val_recall: 0.8919
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.2538 - recall: 0.9334 - val_loss: 0.2751 - val_recall: 0.8829
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2772 - recall: 0.9282 - val_loss: 0.2316 - val_recall: 0.8919
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2665 - recall: 0.9299 - val_loss: 0.1797 - val_recall: 0.8874
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2458 - recall: 0.9328 - val_loss: 0.2056 - val_recall: 0.8874
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2407 - recall: 0.9363 - val_loss: 0.1574 - val_recall: 0.8784
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2325 - recall: 0.9370 - val_loss: 0.1633 - val_recall: 0.8874

print("Time taken in seconds ",end-start)

Time taken in seconds  37.33909344673157

model_1.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 9,476 (37.02 KB)

 Trainable params: 4,737 (18.50 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 4,739 (18.52 KB)

plot(history,"loss")

model_1_train_perf=model_performance_classification(model_1,X_train,y_train)
model_1_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 675us/step

model_1_val_perf=model_performance_classification(model_1,X_val,y_val)
model_1_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 745us/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_2 = Sequential()
model_2.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_2.add(Dense(32,activation="relu"))
model_2.add(Dense(1,activation = 'sigmoid'))
#compiling the model With Adam as optimiser
optimizer = keras.optimizers.Adam()
model_2.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_2.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.8588 - recall: 0.7826 - val_loss: 0.2708 - val_recall: 0.9054
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3898 - recall: 0.9018 - val_loss: 0.2371 - val_recall: 0.9054
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3387 - recall: 0.9118 - val_loss: 0.2323 - val_recall: 0.9009
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3120 - recall: 0.9170 - val_loss: 0.2348 - val_recall: 0.8964
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2956 - recall: 0.9198 - val_loss: 0.2017 - val_recall: 0.8964
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2782 - recall: 0.9208 - val_loss: 0.1886 - val_recall: 0.9009
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2609 - recall: 0.9224 - val_loss: 0.1766 - val_recall: 0.8964
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2499 - recall: 0.9265 - val_loss: 0.1485 - val_recall: 0.8919
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.2417 - recall: 0.9298 - val_loss: 0.1586 - val_recall: 0.8919
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2285 - recall: 0.9304 - val_loss: 0.1449 - val_recall: 0.8829
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2239 - recall: 0.9336 - val_loss: 0.1443 - val_recall: 0.8919
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2147 - recall: 0.9332 - val_loss: 0.1328 - val_recall: 0.8874
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.2037 - recall: 0.9355 - val_loss: 0.1361 - val_recall: 0.8874
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1972 - recall: 0.9375 - val_loss: 0.1543 - val_recall: 0.8919
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1915 - recall: 0.9377 - val_loss: 0.1375 - val_recall: 0.8829
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1841 - recall: 0.9394 - val_loss: 0.1317 - val_recall: 0.8784
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1797 - recall: 0.9436 - val_loss: 0.1608 - val_recall: 0.8919
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1746 - recall: 0.9492 - val_loss: 0.1275 - val_recall: 0.8784
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.1639 - recall: 0.9464 - val_loss: 0.1397 - val_recall: 0.8874
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1587 - recall: 0.9514 - val_loss: 0.1407 - val_recall: 0.8829
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1495 - recall: 0.9498 - val_loss: 0.1272 - val_recall: 0.8784
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1436 - recall: 0.9519 - val_loss: 0.1369 - val_recall: 0.8784
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1457 - recall: 0.9530 - val_loss: 0.1292 - val_recall: 0.8829
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1335 - recall: 0.9560 - val_loss: 0.1585 - val_recall: 0.8874
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1410 - recall: 0.9564 - val_loss: 0.1296 - val_recall: 0.8739
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1282 - recall: 0.9552 - val_loss: 0.1201 - val_recall: 0.8784
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1135 - recall: 0.9624 - val_loss: 0.1183 - val_recall: 0.8784
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1143 - recall: 0.9609 - val_loss: 0.1246 - val_recall: 0.8784
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.1140 - recall: 0.9599 - val_loss: 0.1302 - val_recall: 0.8784
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1084 - recall: 0.9633 - val_loss: 0.1190 - val_recall: 0.8784

print("Time taken in seconds ",end-start)

Time taken in seconds  33.61634564399719

model_2.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,213 (55.52 KB)

 Trainable params: 4,737 (18.50 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 9,476 (37.02 KB)

plot(history,"loss")

model_2_train_perf=model_performance_classification(model_2,X_train,y_train)
model_2_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 705us/step

model_2_val_perf=model_performance_classification(model_2,X_val,y_val)
model_2_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 824us/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_3 = Sequential()
model_3.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_3.add(Dropout(0.4))
model_3.add(Dense(32,activation="relu"))
model_3.add(Dropout(0.2))
model_3.add(Dense(1,activation = 'sigmoid'))
#compiling the model With Adam optimiser
optimizer = keras.optimizers.Adam()
model_3.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_3.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.3322 - recall: 0.6637 - val_loss: 0.3584 - val_recall: 0.8919
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.6297 - recall: 0.8494 - val_loss: 0.3284 - val_recall: 0.8919
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5644 - recall: 0.8693 - val_loss: 0.2597 - val_recall: 0.8784
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4908 - recall: 0.8738 - val_loss: 0.2307 - val_recall: 0.8874
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4413 - recall: 0.8994 - val_loss: 0.2294 - val_recall: 0.8874
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4386 - recall: 0.8874 - val_loss: 0.2230 - val_recall: 0.8964
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4308 - recall: 0.8996 - val_loss: 0.2273 - val_recall: 0.8964
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3995 - recall: 0.8941 - val_loss: 0.2143 - val_recall: 0.8964
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4082 - recall: 0.9002 - val_loss: 0.1864 - val_recall: 0.8964
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4184 - recall: 0.8849 - val_loss: 0.1963 - val_recall: 0.8919
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3834 - recall: 0.8952 - val_loss: 0.1848 - val_recall: 0.8964
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3765 - recall: 0.9043 - val_loss: 0.1827 - val_recall: 0.8874
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3778 - recall: 0.8941 - val_loss: 0.1703 - val_recall: 0.8964
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3544 - recall: 0.9012 - val_loss: 0.1776 - val_recall: 0.8919
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3466 - recall: 0.9055 - val_loss: 0.1565 - val_recall: 0.8919
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3444 - recall: 0.9066 - val_loss: 0.1756 - val_recall: 0.8919
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3449 - recall: 0.9026 - val_loss: 0.1720 - val_recall: 0.8874
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3511 - recall: 0.9044 - val_loss: 0.1699 - val_recall: 0.8874
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3444 - recall: 0.9024 - val_loss: 0.1694 - val_recall: 0.8874
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3283 - recall: 0.9088 - val_loss: 0.1961 - val_recall: 0.8874
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3358 - recall: 0.9073 - val_loss: 0.1749 - val_recall: 0.8919
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3412 - recall: 0.8935 - val_loss: 0.1635 - val_recall: 0.8874
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3268 - recall: 0.9082 - val_loss: 0.1578 - val_recall: 0.8874
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3424 - recall: 0.9088 - val_loss: 0.1650 - val_recall: 0.8874
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3041 - recall: 0.9200 - val_loss: 0.1584 - val_recall: 0.8874
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3453 - recall: 0.8925 - val_loss: 0.1617 - val_recall: 0.8874
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3257 - recall: 0.9083 - val_loss: 0.1633 - val_recall: 0.8829
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3062 - recall: 0.9085 - val_loss: 0.1552 - val_recall: 0.8829
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3075 - recall: 0.9164 - val_loss: 0.1710 - val_recall: 0.8829
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3256 - recall: 0.9125 - val_loss: 0.1580 - val_recall: 0.8919

print("Time taken in seconds ",end-start)

Time taken in seconds  31.806586503982544

model_3.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,213 (55.52 KB)

 Trainable params: 4,737 (18.50 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 9,476 (37.02 KB)

plot(history,"loss")

model_3_train_perf=model_performance_classification(model_3,X_train,y_train)
model_3_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 824us/step

model_3_val_perf=model_performance_classification(model_3,X_val,y_val)
model_3_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_4 = Sequential()
model_4.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_4.add(BatchNormalization())
model_4.add(Dense(32,activation="relu"))
model_4.add(BatchNormalization())
model_4.add(Dense(1,activation = 'sigmoid'))
#compiling the model With Adam as optimiser
optimizer = keras.optimizers.Adam(learning_rate=0.0001)
model_4.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_4.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.6216 - recall: 0.6038 - val_loss: 0.6390 - val_recall: 0.9189
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.9238 - recall: 0.9173 - val_loss: 0.5276 - val_recall: 0.9189
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.7494 - recall: 0.9252 - val_loss: 0.4343 - val_recall: 0.9054
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.6397 - recall: 0.9190 - val_loss: 0.3623 - val_recall: 0.9009
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5604 - recall: 0.9164 - val_loss: 0.3077 - val_recall: 0.8874
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 4ms/step - loss: 0.5020 - recall: 0.9153 - val_loss: 0.2650 - val_recall: 0.8829
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.4585 - recall: 0.9153 - val_loss: 0.2344 - val_recall: 0.8784
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4245 - recall: 0.9137 - val_loss: 0.2105 - val_recall: 0.8739
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3989 - recall: 0.9154 - val_loss: 0.1928 - val_recall: 0.8739
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3781 - recall: 0.9156 - val_loss: 0.1788 - val_recall: 0.8739
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3608 - recall: 0.9152 - val_loss: 0.1677 - val_recall: 0.8739
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3454 - recall: 0.9152 - val_loss: 0.1598 - val_recall: 0.8694
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3326 - recall: 0.9152 - val_loss: 0.1518 - val_recall: 0.8694
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3207 - recall: 0.9178 - val_loss: 0.1459 - val_recall: 0.8649
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3096 - recall: 0.9178 - val_loss: 0.1414 - val_recall: 0.8649
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.2999 - recall: 0.9200 - val_loss: 0.1369 - val_recall: 0.8694
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2898 - recall: 0.9204 - val_loss: 0.1318 - val_recall: 0.8694
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2779 - recall: 0.9232 - val_loss: 0.1291 - val_recall: 0.8649
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2681 - recall: 0.9241 - val_loss: 0.1261 - val_recall: 0.8649
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2599 - recall: 0.9283 - val_loss: 0.1232 - val_recall: 0.8649
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2525 - recall: 0.9283 - val_loss: 0.1206 - val_recall: 0.8649
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2442 - recall: 0.9281 - val_loss: 0.1185 - val_recall: 0.8649
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2372 - recall: 0.9283 - val_loss: 0.1159 - val_recall: 0.8649
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2309 - recall: 0.9330 - val_loss: 0.1144 - val_recall: 0.8649
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2244 - recall: 0.9343 - val_loss: 0.1124 - val_recall: 0.8649
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.2188 - recall: 0.9351 - val_loss: 0.1106 - val_recall: 0.8649
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2128 - recall: 0.9356 - val_loss: 0.1086 - val_recall: 0.8649
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2068 - recall: 0.9404 - val_loss: 0.1072 - val_recall: 0.8649
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2019 - recall: 0.9427 - val_loss: 0.1052 - val_recall: 0.8649
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1962 - recall: 0.9452 - val_loss: 0.1041 - val_recall: 0.8649

print("Time taken in seconds ",end-start)

Time taken in seconds  36.642983198165894

model_4.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 32)             │           128 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,981 (58.52 KB)

 Trainable params: 4,929 (19.25 KB)

 Non-trainable params: 192 (768.00 B)

 Optimizer params: 9,860 (38.52 KB)

plot(history,"loss")

model_4_train_perf=model_performance_classification(model_4,X_train,y_train)
model_4_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 723us/step

model_4_val_perf=model_performance_classification(model_4,X_val,y_val)
model_4_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 926us/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_5 = Sequential()
model_5.add(Dense(64,activation="relu",input_dim = X_train.shape[1]))
model_5.add(BatchNormalization())
model_5.add(Dropout(0.4))
model_5.add(Dense(32,activation="relu"))
model_5.add(BatchNormalization())
model_5.add(Dropout(0.2))
model_5.add(Dense(1,activation = 'sigmoid'))
#compiling the model With Adam as optimiser
optimizer = keras.optimizers.Adam()
model_5.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_5.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.1307 - recall: 0.7977 - val_loss: 0.2494 - val_recall: 0.8784
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.6416 - recall: 0.8748 - val_loss: 0.1924 - val_recall: 0.8739
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5165 - recall: 0.8909 - val_loss: 0.1566 - val_recall: 0.8874
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5173 - recall: 0.8844 - val_loss: 0.1511 - val_recall: 0.8874
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4706 - recall: 0.8970 - val_loss: 0.1374 - val_recall: 0.8829
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4742 - recall: 0.9045 - val_loss: 0.1340 - val_recall: 0.8829
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4519 - recall: 0.9019 - val_loss: 0.1296 - val_recall: 0.8829
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4544 - recall: 0.8974 - val_loss: 0.1223 - val_recall: 0.8829
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4184 - recall: 0.9075 - val_loss: 0.1156 - val_recall: 0.8829
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4317 - recall: 0.8989 - val_loss: 0.1194 - val_recall: 0.8919
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4011 - recall: 0.9074 - val_loss: 0.1189 - val_recall: 0.8874
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4134 - recall: 0.9116 - val_loss: 0.1160 - val_recall: 0.8784
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3797 - recall: 0.9150 - val_loss: 0.1171 - val_recall: 0.8874
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3886 - recall: 0.9014 - val_loss: 0.1130 - val_recall: 0.8964
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3728 - recall: 0.9109 - val_loss: 0.1111 - val_recall: 0.8874
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3694 - recall: 0.9051 - val_loss: 0.1073 - val_recall: 0.8874
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3878 - recall: 0.9084 - val_loss: 0.1064 - val_recall: 0.8874
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3711 - recall: 0.9168 - val_loss: 0.1062 - val_recall: 0.8874
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.3599 - recall: 0.9109 - val_loss: 0.1052 - val_recall: 0.8829
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3674 - recall: 0.9099 - val_loss: 0.1010 - val_recall: 0.8829
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3794 - recall: 0.9046 - val_loss: 0.1014 - val_recall: 0.8829
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3483 - recall: 0.9132 - val_loss: 0.1043 - val_recall: 0.8919
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3609 - recall: 0.9163 - val_loss: 0.1026 - val_recall: 0.8829
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3464 - recall: 0.9084 - val_loss: 0.1021 - val_recall: 0.8874
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3621 - recall: 0.9148 - val_loss: 0.1082 - val_recall: 0.8964
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3311 - recall: 0.9143 - val_loss: 0.1051 - val_recall: 0.8919
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3281 - recall: 0.9236 - val_loss: 0.1040 - val_recall: 0.8919
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.3419 - recall: 0.9151 - val_loss: 0.0971 - val_recall: 0.8919
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.3251 - recall: 0.9160 - val_loss: 0.1006 - val_recall: 0.8919
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3252 - recall: 0.9173 - val_loss: 0.0961 - val_recall: 0.8874

print("Time taken in seconds ",end-start)

Time taken in seconds  36.77445697784424

model_5.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 32)             │           128 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,981 (58.52 KB)

 Trainable params: 4,929 (19.25 KB)

 Non-trainable params: 192 (768.00 B)

 Optimizer params: 9,860 (38.52 KB)

plot(history,"loss")

model_5_train_perf=model_performance_classification(model_5,X_train,y_train)
model_5_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 993us/step

model_5_val_perf=model_performance_classification(model_5,X_val,y_val)
model_5_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 807us/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_6 = Sequential()
model_6.add(Dense(64,activation="relu",input_dim = X_train.shape[1],kernel_initializer='he_normal'))
model_6.add(Dropout(0.4))
model_6.add(Dense(32,activation="relu",kernel_initializer='he_normal'))
model_6.add(Dropout(0.2))
model_6.add(Dense(1,activation = 'sigmoid'))
#compiling the model With Adam as optimiser
optimizer = keras.optimizers.Adam()
model_6.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_6.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.9393 - recall: 0.6566 - val_loss: 0.3760 - val_recall: 0.8829
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.8441 - recall: 0.8062 - val_loss: 0.3082 - val_recall: 0.8874
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.6197 - recall: 0.8560 - val_loss: 0.2725 - val_recall: 0.8829
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5885 - recall: 0.8529 - val_loss: 0.2644 - val_recall: 0.8919
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.5384 - recall: 0.8843 - val_loss: 0.2571 - val_recall: 0.8784
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4891 - recall: 0.8885 - val_loss: 0.2639 - val_recall: 0.8784
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4619 - recall: 0.8862 - val_loss: 0.2133 - val_recall: 0.8784
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.4493 - recall: 0.8742 - val_loss: 0.2134 - val_recall: 0.8874
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4468 - recall: 0.8824 - val_loss: 0.2146 - val_recall: 0.8919
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4250 - recall: 0.8874 - val_loss: 0.2157 - val_recall: 0.8829
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4205 - recall: 0.8945 - val_loss: 0.1953 - val_recall: 0.8829
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.3911 - recall: 0.9025 - val_loss: 0.1974 - val_recall: 0.8919
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3925 - recall: 0.8878 - val_loss: 0.1863 - val_recall: 0.8874
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.4054 - recall: 0.8866 - val_loss: 0.1832 - val_recall: 0.9009
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3833 - recall: 0.8860 - val_loss: 0.1866 - val_recall: 0.9009
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3544 - recall: 0.9095 - val_loss: 0.1765 - val_recall: 0.8964
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3699 - recall: 0.8974 - val_loss: 0.1845 - val_recall: 0.8964
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3412 - recall: 0.9120 - val_loss: 0.1730 - val_recall: 0.8919
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3595 - recall: 0.9030 - val_loss: 0.1667 - val_recall: 0.8874
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3499 - recall: 0.9066 - val_loss: 0.1670 - val_recall: 0.9009
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3585 - recall: 0.9036 - val_loss: 0.1723 - val_recall: 0.8919
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3324 - recall: 0.9080 - val_loss: 0.1634 - val_recall: 0.8874
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3364 - recall: 0.9035 - val_loss: 0.1696 - val_recall: 0.8964
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3326 - recall: 0.9075 - val_loss: 0.1801 - val_recall: 0.9009
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3219 - recall: 0.9076 - val_loss: 0.1701 - val_recall: 0.8919
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3239 - recall: 0.8982 - val_loss: 0.1535 - val_recall: 0.8964
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3150 - recall: 0.9148 - val_loss: 0.1679 - val_recall: 0.8919
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.3318 - recall: 0.9097 - val_loss: 0.1603 - val_recall: 0.8874
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3095 - recall: 0.9121 - val_loss: 0.1662 - val_recall: 0.8919
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3266 - recall: 0.9113 - val_loss: 0.1726 - val_recall: 0.8964

print("Time taken in seconds ",end-start)

Time taken in seconds  35.58531212806702

model_6.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,213 (55.52 KB)

 Trainable params: 4,737 (18.50 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 9,476 (37.02 KB)

plot(history,"loss")

model_6_train_perf=model_performance_classification(model_6,X_train,y_train)
model_6_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 682us/step

model_6_val_perf=model_performance_classification(model_6,X_val,y_val)
model_6_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 770us/step

# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()
#Initializing the neural network
model_7 = Sequential()
model_7.add(Dense(64,activation="relu",input_dim = X_train.shape[1],kernel_initializer='he_normal'))
model_7.add(BatchNormalization())
model_7.add(Dense(32,activation="relu",kernel_initializer='he_normal'))
model_7.add(BatchNormalization())
model_7.add(Dense(1,activation = 'sigmoid'))
#compiling the model with Adam as optimiser
optimizer = keras.optimizers.Adam()
model_7.compile(optimizer =optimizer,loss = 'binary_crossentropy',metrics = ['recall'])
start=time.time()
history=model_7.fit(X_train,y_train,validation_data=(X_val,y_val),epochs=30,batch_size=32,class_weight=cw_dict)
end=time.time()

Epoch 1/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 4s 4ms/step - loss: 0.8269 - recall: 0.8495 - val_loss: 0.2436 - val_recall: 0.8874
Epoch 2/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 3s 4ms/step - loss: 0.4502 - recall: 0.9085 - val_loss: 0.1806 - val_recall: 0.8874
Epoch 3/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3810 - recall: 0.9101 - val_loss: 0.1518 - val_recall: 0.8919
Epoch 4/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3420 - recall: 0.9193 - val_loss: 0.1358 - val_recall: 0.8829
Epoch 5/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.3069 - recall: 0.9251 - val_loss: 0.1351 - val_recall: 0.8919
Epoch 6/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2787 - recall: 0.9344 - val_loss: 0.1294 - val_recall: 0.8919
Epoch 7/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2627 - recall: 0.9336 - val_loss: 0.1273 - val_recall: 0.8919
Epoch 8/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2485 - recall: 0.9327 - val_loss: 0.1219 - val_recall: 0.8964
Epoch 9/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2287 - recall: 0.9410 - val_loss: 0.1196 - val_recall: 0.8919
Epoch 10/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2274 - recall: 0.9392 - val_loss: 0.1166 - val_recall: 0.8919
Epoch 11/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.2068 - recall: 0.9497 - val_loss: 0.1122 - val_recall: 0.8874
Epoch 12/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1832 - recall: 0.9531 - val_loss: 0.1144 - val_recall: 0.8874
Epoch 13/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1739 - recall: 0.9573 - val_loss: 0.1041 - val_recall: 0.8874
Epoch 14/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1623 - recall: 0.9588 - val_loss: 0.1029 - val_recall: 0.8694
Epoch 15/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1551 - recall: 0.9604 - val_loss: 0.0987 - val_recall: 0.8739
Epoch 16/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1395 - recall: 0.9626 - val_loss: 0.1025 - val_recall: 0.8874
Epoch 17/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1260 - recall: 0.9639 - val_loss: 0.0908 - val_recall: 0.8694
Epoch 18/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1334 - recall: 0.9629 - val_loss: 0.0908 - val_recall: 0.8694
Epoch 19/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1130 - recall: 0.9697 - val_loss: 0.1303 - val_recall: 0.8784
Epoch 20/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.1184 - recall: 0.9680 - val_loss: 0.0834 - val_recall: 0.8694
Epoch 21/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0891 - recall: 0.9777 - val_loss: 0.0903 - val_recall: 0.8649
Epoch 22/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.0900 - recall: 0.9766 - val_loss: 0.0883 - val_recall: 0.8649
Epoch 23/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0859 - recall: 0.9758 - val_loss: 0.0891 - val_recall: 0.8694
Epoch 24/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0808 - recall: 0.9809 - val_loss: 0.0867 - val_recall: 0.8423
Epoch 25/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0684 - recall: 0.9847 - val_loss: 0.0953 - val_recall: 0.8649
Epoch 26/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0555 - recall: 0.9896 - val_loss: 0.0862 - val_recall: 0.8514
Epoch 27/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0625 - recall: 0.9825 - val_loss: 0.1080 - val_recall: 0.8468
Epoch 28/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0870 - recall: 0.9816 - val_loss: 0.0921 - val_recall: 0.8694
Epoch 29/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0701 - recall: 0.9832 - val_loss: 0.0928 - val_recall: 0.8739
Epoch 30/30
500/500 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0501 - recall: 0.9902 - val_loss: 0.0931 - val_recall: 0.8694

print("Time taken in seconds ",end-start)

Time taken in seconds  38.600029945373535

model_7.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │         2,624 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 32)             │           128 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 14,981 (58.52 KB)

 Trainable params: 4,929 (19.25 KB)

 Non-trainable params: 192 (768.00 B)

 Optimizer params: 9,860 (38.52 KB)

plot(history,"loss")

#training performance
model_7_train_perf=model_performance_classification(model_7,X_train,y_train)
model_7_train_perf

500/500 ━━━━━━━━━━━━━━━━━━━━ 0s 785us/step

#validation performance
model_7_val_perf=model_performance_classification(model_7,X_val,y_val)
model_7_val_perf

125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 902us/step

#training performance comparision
model_train_perf_comp=pd.concat([model_0_train_perf.T,model_1_train_perf.T,model_2_train_perf.T,
                                 model_3_train_perf.T,model_4_train_perf.T, model_5_train_perf.T,
                                 model_6_train_perf.T,model_7_train_perf.T,
    ],
      axis=1
    )
model_train_perf_comp.columns=[
    "Neural Network (SGD, No Regularization)",
    "Neural Network (SGD with Momentum, No Regularization)",
    "Neural Network (Adam , No Regularization)",
    "Neural Network (Adam, dropout [0.4,0.2])",
    "Neural Network (Adam, Batch Normalization)",
    "Neural Network (Adam,dropout [0.4,0.2], Batch Normalization)",
    "Neural Network (Adam,dropout [0.4,0.2] ,He initialization)",
    "Neural Network (Adam,He initialization, Batch Normalization)"
                               ]
print("Training perfomance comparision:")

model_train_perf_comp

Training perfomance comparision:

#validation perfomance comparision
model_val_perf_comp=pd.concat([model_0_val_perf.T,model_1_val_perf.T,model_2_val_perf.T, model_3_val_perf.T,
                               model_4_val_perf.T,model_5_val_perf.T,model_6_val_perf.T,model_7_val_perf.T,
                               ],axis=1
                              )
model_val_perf_comp.columns=[
    "Neural Network (SGD, No Regularization)",
    "Neural Network (SGD with Momentum, No Regularization)",
    "Neural Network (Adam , No Regularization)",
    "Neural Network (Adam, dropout [0.4,0.2])",
    "Neural Network (Adam, Batch Normalization)",
    "Neural Network (Adam,dropout [0.4,0.2], Batch Normalization)",
    "Neural Network (Adam,dropout [0.4,0.2] ,He initialization)",
    "Neural Network (Adam,He initialization, Batch Normalization)"
                             ]
print("Validation perfomance comparision:")

model_val_perf_comp

Validation perfomance comparision:

#Perfomance of model_3 on test
model_3_test_perf=model_performance_classification(model_3,X_test,y_test)
model_3_test_perf

157/157 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V32	V33	V34	V35	V36	V37	V38	V39	V40
0	-4.464606	-4.679129	3.101546	0.506130	-0.221083	-2.032511	-2.910870	0.050714	-1.522351	3.761892	...	3.059700	-1.690440	2.846296	2.235198	6.667486	0.443809	-2.369169	2.950578	-3.480324
1	3.365912	3.653381	0.909671	-1.367528	0.332016	2.358938	0.732600	-4.332135	0.565695	-0.101080	...	-1.795474	3.032780	-2.467514	1.894599	-2.297780	-1.731048	5.908837	-0.386345	0.616242
2	-3.831843	-5.824444	0.634031	-2.418815	-1.773827	1.016824	-2.098941	-3.173204	-2.081860	5.392621	...	-0.257101	0.803550	4.086219	2.292138	5.360850	0.351993	2.940021	3.839160	-4.309402
3	1.618098	1.888342	7.046143	-1.147285	0.083080	-1.529780	0.207309	-2.493629	0.344926	2.118578	...	-3.584425	-2.577474	1.363769	0.622714	5.550100	-1.526796	0.138853	3.101430	-1.277378
4	-0.111440	3.872488	-3.758361	-2.982897	3.792714	0.544960	0.205433	4.848994	-1.854920	-6.220023	...	8.265896	6.629213	-10.068689	1.222987	-3.229763	1.686909	-2.163896	-3.644622	6.510338

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V32	V33	V34	V35	V36	V37	V38	V39	V40
0	-0.613489	-3.819640	2.202302	1.300420	-1.184929	-4.495964	-1.835817	4.722989	1.206140	-0.341909	...	2.291204	-5.411388	0.870073	0.574479	4.157191	1.428093	-10.511342	0.454664	-1.448363
1	0.389608	-0.512341	0.527053	-2.576776	-1.016766	2.235112	-0.441301	-4.405744	-0.332869	1.966794	...	-2.474936	2.493582	0.315165	2.059288	0.683859	-0.485452	5.128350	1.720744	-1.488235
2	-0.874861	-0.640632	4.084202	-1.590454	0.525855	-1.957592	-0.695367	1.347309	-1.732348	0.466500	...	-1.318888	-2.997464	0.459664	0.619774	5.631504	1.323512	-1.752154	1.808302	1.675748
3	0.238384	1.458607	4.014528	2.534478	1.196987	-3.117330	-0.924035	0.269493	1.322436	0.702345	...	3.517918	-3.074085	-0.284220	0.954576	3.029331	-1.367198	-3.412140	0.906000	-2.450889
4	5.828225	2.768260	-1.234530	2.809264	-1.641648	-1.406698	0.568643	0.965043	1.918379	-2.774855	...	1.773841	-1.501573	-2.226702	4.776830	-6.559698	-0.805551	-0.276007	-3.858207	-0.537694

	count	mean	std	min	25%	50%	75%	max
V1	19982.0	-0.271996	3.441625	-11.876451	-2.737146	-0.747917	1.840112	15.493002
V2	19982.0	0.440430	3.150784	-12.319951	-1.640674	0.471536	2.543967	13.089269
V3	20000.0	2.484699	3.388963	-10.708139	0.206860	2.255786	4.566165	17.090919
V4	20000.0	-0.083152	3.431595	-15.082052	-2.347660	-0.135241	2.130615	13.236381
V5	20000.0	-0.053752	2.104801	-8.603361	-1.535607	-0.101952	1.340480	8.133797
V6	20000.0	-0.995443	2.040970	-10.227147	-2.347238	-1.000515	0.380330	6.975847
V7	20000.0	-0.879325	1.761626	-7.949681	-2.030926	-0.917179	0.223695	8.006091
V8	20000.0	-0.548195	3.295756	-15.657561	-2.642665	-0.389085	1.722965	11.679495
V9	20000.0	-0.016808	2.160568	-8.596313	-1.494973	-0.067597	1.409203	8.137580
V10	20000.0	-0.012998	2.193201	-9.853957	-1.411212	0.100973	1.477045	8.108472
V11	20000.0	-1.895393	3.124322	-14.832058	-3.922404	-1.921237	0.118906	11.826433
V12	20000.0	1.604825	2.930454	-12.948007	-0.396514	1.507841	3.571454	15.080698
V13	20000.0	1.580486	2.874658	-13.228247	-0.223545	1.637185	3.459886	15.419616
V14	20000.0	-0.950632	1.789651	-7.738593	-2.170741	-0.957163	0.270677	5.670664
V15	20000.0	-2.414993	3.354974	-16.416606	-4.415322	-2.382617	-0.359052	12.246455
V16	20000.0	-2.925225	4.221717	-20.374158	-5.634240	-2.682705	-0.095046	13.583212
V17	20000.0	-0.134261	3.345462	-14.091184	-2.215611	-0.014580	2.068751	16.756432
V18	20000.0	1.189347	2.592276	-11.643994	-0.403917	0.883398	2.571770	13.179863
V19	20000.0	1.181808	3.396925	-13.491784	-1.050168	1.279061	3.493299	13.237742
V20	20000.0	0.023608	3.669477	-13.922659	-2.432953	0.033415	2.512372	16.052339
V21	20000.0	-3.611252	3.567690	-17.956231	-5.930360	-3.532888	-1.265884	13.840473
V22	20000.0	0.951835	1.651547	-10.122095	-0.118127	0.974687	2.025594	7.409856
V23	20000.0	-0.366116	4.031860	-14.866128	-3.098756	-0.262093	2.451750	14.458734
V24	20000.0	1.134389	3.912069	-16.387147	-1.468062	0.969048	3.545975	17.163291
V25	20000.0	-0.002186	2.016740	-8.228266	-1.365178	0.025050	1.397112	8.223389
V26	20000.0	1.873785	3.435137	-11.834271	-0.337863	1.950531	4.130037	16.836410
V27	20000.0	-0.612413	4.368847	-14.904939	-3.652323	-0.884894	2.189177	17.560404
V28	20000.0	-0.883218	1.917713	-9.269489	-2.171218	-0.891073	0.375884	6.527643
V29	20000.0	-0.985625	2.684365	-12.579469	-2.787443	-1.176181	0.629773	10.722055
V30	20000.0	-0.015534	3.005258	-14.796047	-1.867114	0.184346	2.036229	12.505812
V31	20000.0	0.486842	3.461384	-13.722760	-1.817772	0.490304	2.730688	17.255090
V32	20000.0	0.303799	5.500400	-19.876502	-3.420469	0.052073	3.761722	23.633187
V33	20000.0	0.049825	3.575285	-16.898353	-2.242857	-0.066249	2.255134	16.692486
V34	20000.0	-0.462702	3.183841	-17.985094	-2.136984	-0.255008	1.436935	14.358213
V35	20000.0	2.229620	2.937102	-15.349803	0.336191	2.098633	4.064358	15.291065
V36	20000.0	1.514809	3.800860	-14.833178	-0.943809	1.566526	3.983939	19.329576
V37	20000.0	0.011316	1.788165	-5.478350	-1.255819	-0.128435	1.175533	7.467006
V38	20000.0	-0.344025	3.948147	-17.375002	-2.987638	-0.316849	2.279399	15.289923
V39	20000.0	0.890653	1.753054	-6.438880	-0.272250	0.919261	2.057540	7.759877
V40	20000.0	-0.875630	3.012155	-11.023935	-2.940193	-0.920806	1.119897	10.654265
Target	20000.0	0.055500	0.228959	0.000000	0.000000	0.000000	0.000000	1.000000

	Neural Network (SGD, No Regularization)	Neural Network (SGD with Momentum, No Regularization)	Neural Network (Adam , No Regularization)	Neural Network (Adam, dropout [0.4,0.2])	Neural Network (Adam, Batch Normalization)	Neural Network (Adam,dropout [0.4,0.2], Batch Normalization)	Neural Network (Adam,dropout [0.4,0.2] ,He initialization)	Neural Network (Adam,He initialization, Batch Normalization)
Accuracy	0.981750	0.993437	0.987812	0.990688	0.986250	0.989375	0.990938	0.986812
Recall	0.981750	0.993437	0.987812	0.990688	0.986250	0.989375	0.990938	0.986812
Precision	0.984608	0.993372	0.989453	0.990692	0.986826	0.989473	0.990923	0.987221
F1 Score	0.982628	0.993393	0.988276	0.990690	0.986475	0.989420	0.990930	0.986979

	Neural Network (SGD, No Regularization)	Neural Network (SGD with Momentum, No Regularization)	Neural Network (Adam , No Regularization)	Neural Network (Adam, dropout [0.4,0.2])	Neural Network (Adam, Batch Normalization)	Neural Network (Adam,dropout [0.4,0.2], Batch Normalization)	Neural Network (Adam,dropout [0.4,0.2] ,He initialization)	Neural Network (Adam,He initialization, Batch Normalization)
Accuracy	0.978000	0.988000	0.979250	0.989250	0.982750	0.9875	0.9885	0.984250
Recall	0.978000	0.988000	0.979250	0.989250	0.982750	0.9875	0.9885	0.984250
Precision	0.980238	0.987951	0.980815	0.989148	0.983112	0.9875	0.9885	0.984428
F1 Score	0.978812	0.987974	0.979846	0.989192	0.982911	0.9875	0.9885	0.984332

Data Description¶

Installing and Importing the necessary libraries¶

Loading the Data¶

Data Overview¶

Understand shape of the dataset¶

View the first 5 rows of the datasets¶

check the datatypes of column of the datasets¶

Checking for missing values¶

Checking for duplicates¶

Let's check the statistical summary of the data¶

Exploratory Data Analysis¶

Univariate analysis¶

Distribution of target¶

Bivariate Analysis¶

Creating functions that will help us with further analysis.¶

Data Preprocessing¶

Separating response variable and predictors in train dataset¶

Splitting the train dataset into train and Validation dataset¶

Separating response variable and predictors in test dataset¶

Imputing missing values¶

Model Building¶

Model Evaluation Criterion¶

Utility functions¶

dataframe to store the results from all the models we build¶

Initial Model Building (Model 0)¶

Model Performance Improvement¶

Model 1¶

Model 2¶

Model 3¶

Model 4¶

Model 5¶

Model 6¶

Model 7¶

Model Performance Comparison and Final Model Selection¶

Actionable Insights and Recommendations¶

	proportion
Target
0	0.9445
1	0.0555

	proportion
Target
0	0.9436
1	0.0564